In the context of Agile Combat Employment (ACE), what strategies and modifications can be implemented in the Combat Information Network (CIN) and Mission Planning Team (MPT) workflows to increase efficiency, resilience, agility, and decrease waste in intelligence support operations? Is there a simplified workflow that maintains situational awareness and operational alignment with reduced personnel and meeting frequency, and what is the minimum viable intelligence support team?
As a potential solution to making these workflows more efficient, can artificial intelligence (AI) be harnessed to analyze forensic data and patterns of life to assist the Intelligence, Surveillance, and Reconnaissance Division (ISRD) in building ISR packages? Furthermore, can AI analyze real-time data to assist in the dynamic re-tasking of existing assets currently operating in theater?
- AU Library Libguide - Military Applications of Artificial Intelligence
- Baird, Lt. Col. Michael D., "Implications of Artificial Intelligence Integration into Intelligence, Surveillance and Reconnaissance Operations," eSchool thesis, Master of Joint Warfare, 2020, 40 pgs.
- Bauman, Maj. Nicholas J., "Reaper Retirement: Why the Air Force Should Reconsider Plans to Cut the MQ-9," AFGC thesis, 2023.
- Answered by Bauman's proposal to leverage artificial intelligence and computer vision to streamline intelligence exploitation workflows. He explains that a major operational bottleneck for the MQ-9 is its ability to collect an enormous volume of full-motion video and imagery during its extended 20+ hour missions, which human crews and intelligence analysts cannot process and exploit quickly. To overcome these manual delays and reduce data-load on operators, Bauman supports the integration of AI tools, specifically pointing to "Project Maven"—a Defense Department initiative designed to automatically process RPA imagery and detect potential targets. By integrating Project Maven's outputs directly into workflows, analysts and crews can leverage machine learning for real-time change detection, automated target identification, and data fusion, which dramatically accelerates the find, fix, and target cycle while maintaining maximum situational awareness with a minimized personnel footprint.
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- Rogers, Capt. Ilya K., "Data Annotation with DBSCAN and GMM Unsupervised Clustering using Flow Cytometry Slides," SOS AUAR 2021, 9 pgs.
- Sturtevant, Capt. Chelsey, "AI-HyperCal: In-Scene Hyperspectral Imagery Calibration Using AI Known-Point Identification," SOS AUAR, 2021, 11 pgs.
- Thomas, Maj. Jacob M., "The Military Internet of Things: Adapting Commercial Capabilities," Air Force Fellows 2021, 43 pgs.
- Albanese, Capt. Stephanie M., "Restructuring Air Force Artificial Intelligence/Machine Learning Efforts to Enable Short- and Long-Term Decision-Making Advantage," SOS AUAR 2025, 15 pgs.